A Novel Exhibition Case Description Method of Virtual Computer-Aided Design

  • Xinyue Wang
  • Xue Gao
  • Yue LiuEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)


In a museum a large number of cultural, historical or scientific objects are kept and shown to the public. As people’s requirements for culture become increasingly urgent, to offer visitors a better user experience attracts more attentions, which places greater demands on the abilities of museum’s exhibition designers. However, during the exhibition design process of traditional museums, exhibition designers have difficulties in reusing and referring to the previous design cases. This paper presents a case description method which can be applied to exhibition design of museums. The case-based reasoning (CBR) method is introduced to retrieve and obtain similar cases, which are then provided to the designers for reference. These cases are described from three aspects, which are exhibition information, exhibits and exhibition halls. The case information retrieval based on floor plans of museum exhibition hall is realized by image processing and feature extraction. The experimental results show that the proposed method can help exhibition designers retrieve effective and accurate design cases for reference.


Case-based reasoning Museum exhibition design Levenshtein distance Image processing Feature extraction 



This work has been supported by the National Technology Support Program of China (Grant No. 2015BAK01B05).


  1. 1.
    Chunxia, Y.: The integration and expansion of the functions for the museum. Technol. Pioneers 2, 202 (2013)Google Scholar
  2. 2.
    Weber, M., Liwicki, M., Dengel, A.: a.SCAtch - a sketch-based retrieval for architectural floor plans. In: 2010 International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 289–294. IEEE (2010)Google Scholar
  3. 3.
    Ayzenshtadt, V., Langenhan, C., Bukhari, S.S., et al.: Distributed domain model for the case-based retrieval of architectural building designs. In: Proceedings of the 20th UK Workshop on Case-Based Reasoning, UK Workshop on Case-Based Reasoning (UKCBR-2015), Located at SGAI International Conference on Artificial Intelligence, pp. 15–17, December 2015Google Scholar
  4. 4.
    Langenhan, C., Petzold, F.: The fingerprint of architecture-sketch-based design methods for researching building layouts through the semantic fingerprinting of floor plans. Int. Electron. Sci.-Educ. J.: Architect. Mod. Inf. Technol. 4, 13 (2010)Google Scholar
  5. 5.
    Sabri, Q.U., Bayer, J., Ayzenshtadt, V., et al.: Semantic pattern-based retrieval of architectural floor plans with case-based and graph-based searching techniques and their evaluation and visualization. In: ICPRAM, pp. 50–60 (2017)Google Scholar
  6. 6.
    Langenhan, C., Weber, M., Liwicki, M., et al.: Sketch-based methods for researching building layouts through the semantic fingerprint of architecture. In: Computer-Aided Architectural Design Futures (2011)Google Scholar
  7. 7.
    Ahmed, S., Weber, M., Liwicki, M., et al.: Automatic analysis and sketch-based retrieval of architectural floor plans. Pattern Recogn. Lett. 35, 91–100 (2014)CrossRefGoogle Scholar
  8. 8.
    Ahmed, S., Liwicki, M., Weber, M., et al.: Improved automatic analysis of architectural floor plans. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 864–869. IEEE (2011)Google Scholar
  9. 9.
    Ahmed, S., Weber, M., Liwicki, M., et al.: Text/graphics segmentation in architectural floor plans. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 734–738. IEEE (2011)Google Scholar
  10. 10.
    Tombre, K., Tabbone, S., Pélissier, L., Lamiroy, B., Dosch, P.: Text/graphics separation revisited. In: Lopresti, D., Hu, J., Kashi, R. (eds.) DAS 2002. LNCS, vol. 2423, pp. 200–211. Springer, Heidelberg (2002). Scholar
  11. 11.
    Fletcher, L.A., Kasturi, R.: A robust algorithm for text string separation from mixed text/graphics images. IEEE Trans. Pattern Anal. Mach. Intell. 10(6), 910–918 (1988)CrossRefGoogle Scholar
  12. 12.
    Bay, H., Ess, A., Tuytelaars, T., et al.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  13. 13.
    Shanlin, Y., Zhiwei, N.: Machine learning and intelligent decision support system, Beijing, Science Edition (2004)Google Scholar
  14. 14.
    Jian, C., Zhe, T., Zhenxing, L.: A review and analysis of case-based reasoning research. In: 2015 International Conference on Intelligent Transportation, Big Data and Smart City (ICITBS), pp. 51–55. IEEE (2015)Google Scholar
  15. 15.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)Google Scholar
  16. 16.
    Macé, S., Locteau, H., Valveny, E., et al.: A system to detect rooms in architectural floor plan images. In: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, pp. 167–174. ACM (2010)Google Scholar
  17. 17.
    Bin, C., Jianwei, Y., Huirui, C.: Process retrieval method based on Levenshtein distance. Comput. Integr. Manuf. Syst. (CIMS) 18(8), 1766–1773 (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Beijing Engineering Research Center of Mixed Reality and Advanced DisplayBeijingChina
  2. 2.School of OptoelectronicsBeijing Institute of TechnologyBeijingChina

Personalised recommendations